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. 2022 Apr 25;3(4):505–517. doi: 10.1038/s43018-022-00356-3

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© The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 6 — Scatter plot shows Spearman’s correlation (left), and Pearson’s correlation (right), between gene expression estimated by BayesPrism (red), total bulk (blue), CIBERSORTx group mode (orange) or CIBERSORTx high resolution mode (purple) and the average expression from malignant cells in scRNA-seq as a function of the fraction of malignant cells in the dataset containing the 270 simulated samples. The correlation coefficient was calculated on 53 imputable genes by the high-resolution mode out of the top 1000 most variable genes in malignant cells. Spearman’s correlation coefficients were calculated on untransformed gene expression values, while Pearson’s correlation coefficients were calculated on variance-stabilizing transformed gene expression values.